Why Bigger Models Won’t Win the Enterprise AI Race

By Benoit Bouffard, CPO at LightOn

Walking through the aisles of the Grand Palais during Adopt AI Paris this year, the energy was palpable. The industry is buzzing with promises of "bigger," "faster," and "billions of parameters." The prevailing narrative is clear: the race to Artificial General Intelligence (AGI) is on, and the winner will be the one with the biggest brain.

But during my keynote, I shared a conviction that goes against this grain, a conviction forged through our daily work with CIOs and enterprise leaders.

LLMs are not as important as you think.

While everyone is obsessed with the model, we believe the future of Enterprise AI isn't about the brain, it's about the memory.

1. The Model Illusion

There is a widespread illusion in our market. We tend to believe that to get smarter AI, we need a model that has "read" more of the internet. We look for a generic genius capable of reciting Wikipedia or passing the bar exam.

But let’s be honest about enterprise needs. Does your company need a genius that knows the date of the Battle of Waterloo? No. You need an intelligence that understands your strategy, your clients, and your internal processes.

The model is just the voice. It provides the syntax, the grammar, and the fluency. But the intelligence, the competitive advantage, comes strictly from your data.

Imagine hiring a top-tier consultant but refusing to show them a single internal file. They might be brilliant, but without context, they will guess. In AI terms, they will hallucinate. To be useful, AI must stop reciting its training data and start reading your company's reality.

2. The Real Challenge: Fragmentation

For a long time, the debate was "Cloud vs. On-Premise." But the reality we discussed at Adopt AI is more complex. The real enemy of enterprise AI is Data Fragmentation.

Your data isn't sitting neatly in one secure vault. It is scattered. It lives in your CRM, your Document Management System (GED), your internal chats, and countless SQL databases. On average, an enterprise manages more than 100 data sources.

Worse, 80% of this data is unstructured. It’s hidden in PDFs, meeting transcripts, and slide decks.

The "Enterprise Paradox" isn't just about security; it's about connectivity. How do you reconnect this fragmented, unstructured knowledge to the AI without breaking the bank or your compliance rules?

3. RAG Demystified: The Open Book Exam

This is where RAG (Retrieval-Augmented Generation) becomes the cornerstone of the architecture.

To explain it simply, as I did on stage:

• Standard LLM: Think of it as a student taking a Closed Book Exam. They must rely entirely on their memory. If they forget a fact, they invent one to pass. This is risky for business.
• RAG: This allows the student to take an Open Book Exam. Before answering, the student allows to go to the library, find the exact textbook, open it to the right page, and read the answer.

By transforming your documents into mathematical vectors, we allow the AI to understand the "meaning" behind your data, ensuring it finds the right page every time.

With RAG, the AI doesn't guess. It cites its sources.

4. "RAG to Riches": Context Over Model Size

One of the key takeaways from the event was the discussion around cost and efficiency. Some players suggest solving the memory problem by increasing the "Context Window" feeding the model millions of tokens (entire books) for every single query.

We call this the "RAG to Riches" mistake. Processing 1 million tokens for every question is slow, energy-intensive, and incredibly expensive.

Why pay to process an entire book when you only need one paragraph?Efficiency is the new performance. By using a smart, sovereign retrieval engine, we send only the relevant context to the model. We transform a cost center into a value center, achieving impact without extravagance.

5. Privacy is Foundational

Finally, we addressed the "Elephant in the room": Privacy.
‍66% of GenAI use cases depend on search infrastructure. Yet, as soon as AI touches sensitive data, HR records, R&D patents, financial audits, enterprises hit a wall.

Even if you trust your cloud provider, you need a search layer that acts as a secure bridge across all your silos, respecting the access rights of every user. At LightOn, our mission is to enable those who say, "My data must never leave my house," to access the best of AI.

Conclusion

The war for bigger models, the one being fought by the American giants, is not your war. Your battle is about mobilizing your fragmented, unstructured data to create value.

As we wrap up this edition of Adopt AI, our message remains: Don't wait for a bigger brain. Build a better memory.

Ready to transform your scattered data into a strategic asset?
Discover how LightOn's Paradigm platform masters retrieval for the enterprise.